7 ноября 2024 год, ННГУ практика
ВерловНА
2024-11-07
Анисимова Полина
Данные от П. Анисимовой измерения массы муравьев (мг) в желудках ящериц в зависимости от времени года:
library(readxl)## Warning: пакет 'readxl' был собран под R версии 4.2.3DataSet1 <- read_excel("Data/Анисимова данные.xlsx")
summary(DataSet1)## month biomass
## Length:24 Min. : 0.00
## Class :character 1st Qu.: 16.75
## Mode :character Median : 51.00
## Mean : 201.04
## 3rd Qu.: 235.25
## Max. :1889.00Чуть добавим анализа
mean(DataSet1$biomass)## [1] 201.0417sd(DataSet1$biomass)## [1] 399.6585min(DataSet1$biomass)## [1] 0max(DataSet1$biomass)## [1] 1889quantile(DataSet1$biomass)## 0% 25% 50% 75% 100%
## 0.00 16.75 51.00 235.25 1889.00quantile(DataSet1$biomass, probs = c(10, 90)/100)## 10% 90%
## 5.3 506.9Построим гистограмму распредеелния
hist(DataSet1$biomass, breaks = 20)
#install.packages("gtsummary")
library(gtsummary)## Warning: пакет 'gtsummary' был собран под R версии 4.2.3library(tidyverse)## Warning: пакет 'tidyverse' был собран под R версии 4.2.3## Warning: пакет 'ggplot2' был собран под R версии 4.2.3## Warning: пакет 'tibble' был собран под R версии 4.2.3## Warning: пакет 'readr' был собран под R версии 4.2.3## Warning: пакет 'purrr' был собран под R версии 4.2.3## Warning: пакет 'dplyr' был собран под R версии 4.2.3## Warning: пакет 'lubridate' был собран под R версии 4.2.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsDataSet1 %>% filter(month == "June")## # A tibble: 3 × 2
## month biomass
## <chr> <dbl>
## 1 June 13
## 2 June 242
## 3 June 105tbl1 <- DataSet1 %>% tbl_summary()
tbl1| Characteristic | N = 241 |
|---|---|
| month | |
| August | 10 (42%) |
| July | 5 (21%) |
| June | 3 (13%) |
| September | 6 (25%) |
| biomass | 51 (17, 235) |
| 1 n (%); Median (IQR) | |
tbl1 %>% as_flex_table() %>%
flextable::save_as_docx( path = "tbl1_tbl.docx") tbl1_2 <- DataSet1 %>% tbl_summary(by = month)
tbl1_2| Characteristic | August, N = 101 | July, N = 51 | June, N = 31 | September, N = 61 |
|---|---|---|---|---|
| biomass | 161 (60, 508) | 20 (8, 59) | 105 (59, 174) | 12 (5, 20) |
| 1 Median (IQR) | ||||
Шелепин Никита
Данные по осадкам в Австралии в зависимости от места - времени.
library(readr)
DataSet2 <- read_csv("Data/Шелепин.csv")## Rows: 145460 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Location, WindGustDir, WindDir9am, WindDir3pm, RainToday, RainTom...
## dbl (16): MinTemp, MaxTemp, Rainfall, Evaporation, Sunshine, WindGustSpeed,...
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#summary(DataSet2)Выберем из загруженных данных интересующие нас
names(DataSet2)## [1] "Date" "Location" "MinTemp" "MaxTemp"
## [5] "Rainfall" "Evaporation" "Sunshine" "WindGustDir"
## [9] "WindGustSpeed" "WindDir9am" "WindDir3pm" "WindSpeed9am"
## [13] "WindSpeed3pm" "Humidity9am" "Humidity3pm" "Pressure9am"
## [17] "Pressure3pm" "Cloud9am" "Cloud3pm" "Temp9am"
## [21] "Temp3pm" "RainToday" "RainTomorrow"DataSet2 <- DataSet2 %>% select("Location", "MinTemp", "MaxTemp", "Rainfall")
summary(DataSet2)## Location MinTemp MaxTemp Rainfall
## Length:145460 Min. :-8.50 Min. :-4.80 Min. : 0.000
## Class :character 1st Qu.: 7.60 1st Qu.:17.90 1st Qu.: 0.000
## Mode :character Median :12.00 Median :22.60 Median : 0.000
## Mean :12.19 Mean :23.22 Mean : 2.361
## 3rd Qu.:16.90 3rd Qu.:28.20 3rd Qu.: 0.800
## Max. :33.90 Max. :48.10 Max. :371.000
## NA's :1485 NA's :1261 NA's :3261Сделаем описательную статистику для выбранных данных
tbl2 <- DataSet2 %>% tbl_summary() #описательная статистика
tbl2| Characteristic | N = 145,4601 |
|---|---|
| Location | |
| Adelaide | 3,193 (2.2%) |
| Albany | 3,040 (2.1%) |
| Albury | 3,040 (2.1%) |
| AliceSprings | 3,040 (2.1%) |
| BadgerysCreek | 3,009 (2.1%) |
| Ballarat | 3,040 (2.1%) |
| Bendigo | 3,040 (2.1%) |
| Brisbane | 3,193 (2.2%) |
| Cairns | 3,040 (2.1%) |
| Canberra | 3,436 (2.4%) |
| Cobar | 3,009 (2.1%) |
| CoffsHarbour | 3,009 (2.1%) |
| Dartmoor | 3,009 (2.1%) |
| Darwin | 3,193 (2.2%) |
| GoldCoast | 3,040 (2.1%) |
| Hobart | 3,193 (2.2%) |
| Katherine | 1,578 (1.1%) |
| Launceston | 3,040 (2.1%) |
| Melbourne | 3,193 (2.2%) |
| MelbourneAirport | 3,009 (2.1%) |
| Mildura | 3,009 (2.1%) |
| Moree | 3,009 (2.1%) |
| MountGambier | 3,040 (2.1%) |
| MountGinini | 3,040 (2.1%) |
| Newcastle | 3,039 (2.1%) |
| Nhil | 1,578 (1.1%) |
| NorahHead | 3,004 (2.1%) |
| NorfolkIsland | 3,009 (2.1%) |
| Nuriootpa | 3,009 (2.1%) |
| PearceRAAF | 3,009 (2.1%) |
| Penrith | 3,039 (2.1%) |
| Perth | 3,193 (2.2%) |
| PerthAirport | 3,009 (2.1%) |
| Portland | 3,009 (2.1%) |
| Richmond | 3,009 (2.1%) |
| Sale | 3,009 (2.1%) |
| SalmonGums | 3,001 (2.1%) |
| Sydney | 3,344 (2.3%) |
| SydneyAirport | 3,009 (2.1%) |
| Townsville | 3,040 (2.1%) |
| Tuggeranong | 3,039 (2.1%) |
| Uluru | 1,578 (1.1%) |
| WaggaWagga | 3,009 (2.1%) |
| Walpole | 3,006 (2.1%) |
| Watsonia | 3,009 (2.1%) |
| Williamtown | 3,009 (2.1%) |
| Witchcliffe | 3,009 (2.1%) |
| Wollongong | 3,040 (2.1%) |
| Woomera | 3,009 (2.1%) |
| MinTemp | 12 (8, 17) |
| Unknown | 1,485 |
| MaxTemp | 23 (18, 28) |
| Unknown | 1,261 |
| Rainfall | 0.0 (0.0, 0.8) |
| Unknown | 3,261 |
| 1 n (%); Median (IQR) | |
Сделаем сравнение между штатами
tbl2 <- DataSet2 %>% tbl_summary(by = Location) #описательная статистика + разбивка по Location
tbl2| Characteristic | Adelaide, N = 3,1931 | Albany, N = 3,0401 | Albury, N = 3,0401 | AliceSprings, N = 3,0401 | BadgerysCreek, N = 3,0091 | Ballarat, N = 3,0401 | Bendigo, N = 3,0401 | Brisbane, N = 3,1931 | Cairns, N = 3,0401 | Canberra, N = 3,4361 | Cobar, N = 3,0091 | CoffsHarbour, N = 3,0091 | Dartmoor, N = 3,0091 | Darwin, N = 3,1931 | GoldCoast, N = 3,0401 | Hobart, N = 3,1931 | Katherine, N = 1,5781 | Launceston, N = 3,0401 | Melbourne, N = 3,1931 | MelbourneAirport, N = 3,0091 | Mildura, N = 3,0091 | Moree, N = 3,0091 | MountGambier, N = 3,0401 | MountGinini, N = 3,0401 | Newcastle, N = 3,0391 | Nhil, N = 1,5781 | NorahHead, N = 3,0041 | NorfolkIsland, N = 3,0091 | Nuriootpa, N = 3,0091 | PearceRAAF, N = 3,0091 | Penrith, N = 3,0391 | Perth, N = 3,1931 | PerthAirport, N = 3,0091 | Portland, N = 3,0091 | Richmond, N = 3,0091 | Sale, N = 3,0091 | SalmonGums, N = 3,0011 | Sydney, N = 3,3441 | SydneyAirport, N = 3,0091 | Townsville, N = 3,0401 | Tuggeranong, N = 3,0391 | Uluru, N = 1,5781 | WaggaWagga, N = 3,0091 | Walpole, N = 3,0061 | Watsonia, N = 3,0091 | Williamtown, N = 3,0091 | Witchcliffe, N = 3,0091 | Wollongong, N = 3,0401 | Woomera, N = 3,0091 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MinTemp | 12 (9, 16) | 13 (10, 16) | 9 (5, 14) | 14 (7, 20) | 11 (7, 16) | 7 (4, 10) | 8 (4, 13) | 17 (13, 20) | 22 (19, 24) | 7 (2, 12) | 13 (7, 19) | 15 (11, 19) | 9 (6, 11) | 24 (22, 25) | 18 (14, 21) | 9 (6, 12) | 22 (17, 25) | 8 (4, 12) | 11 (9, 15) | 10 (7, 13) | 10 (6, 15) | 14 (7, 19) | 9 (6, 11) | 3 (-1, 8) | 14 (10, 18) | 8 (5, 12) | 16 (12, 19) | 17 (15, 19) | 9 (6, 13) | 12 (9, 16) | 13 (8, 17) | 13 (9, 17) | 13 (9, 16) | 10 (7, 12) | 12 (7, 17) | 8 (5, 12) | 9 (5, 14) | 15 (11, 19) | 15 (11, 19) | 22 (17, 24) | 7 (2, 12) | 15 (8, 21) | 9 (4, 15) | 12 (9, 14) | 10 (7, 13) | 13 (9, 17) | 11 (8, 14) | 15 (12, 18) | 13 (8, 18) |
| Unknown | 2 | 63 | 11 | 1 | 36 | 1 | 2 | 9 | 1 | 6 | 6 | 15 | 69 | 1 | 3 | 0 | 49 | 6 | 480 | 0 | 0 | 2 | 2 | 91 | 346 | 5 | 30 | 1 | 11 | 22 | 28 | 0 | 0 | 9 | 20 | 1 | 41 | 4 | 1 | 2 | 1 | 35 | 0 | 35 | 7 | 2 | 9 | 15 | 4 |
| MaxTemp | 22 (17, 28) | 20 (18, 22) | 22 (16, 29) | 30 (23, 36) | 23 (19, 28) | 17 (12, 23) | 21 (15, 27) | 27 (23, 29) | 30 (28, 31) | 20 (15, 26) | 26 (19, 32) | 24 (21, 27) | 18 (15, 23) | 33 (32, 34) | 26 (23, 29) | 17 (14, 21) | 35 (33, 37) | 19 (15, 23) | 20 (16, 24) | 19 (15, 25) | 24 (18, 31) | 27 (21, 33) | 19 (15, 23) | 12 (6, 17) | 24 (20, 28) | 21 (16, 28) | 23 (19, 26) | 22 (20, 24) | 21 (15, 27) | 25 (20, 31) | 24 (20, 29) | 24 (20, 29) | 25 (20, 30) | 17 (14, 20) | 24 (20, 29) | 19 (16, 24) | 24 (19, 29) | 23 (20, 26) | 23 (20, 27) | 30 (27, 31) | 20 (15, 26) | 31 (24, 37) | 23 (16, 29) | 20 (17, 23) | 20 (15, 25) | 24 (20, 27) | 21 (18, 25) | 21 (18, 24) | 26 (20, 33) |
| Unknown | 3 | 54 | 11 | 2 | 29 | 1 | 5 | 14 | 0 | 3 | 3 | 19 | 63 | 1 | 7 | 1 | 40 | 6 | 481 | 0 | 0 | 0 | 5 | 53 | 235 | 6 | 30 | 1 | 9 | 21 | 25 | 1 | 0 | 1 | 14 | 1 | 40 | 2 | 0 | 1 | 4 | 7 | 0 | 39 | 0 | 3 | 6 | 11 | 3 |
| Rainfall | 0.0 (0.0, 0.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.4) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.6) | 0.0 (0.0, 2.6) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.0) | 0.0 (0.0, 2.0) | 0.2 (0.0, 1.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 1.2) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.8) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.8) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.2 (0.0, 1.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.2) | 0.0 (0.0, 1.6) | 0.2 (0.0, 1.8) | 0.0 (0.0, 0.5) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.2) | 0.2 (0.0, 2.6) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.8) | 0.0 (0.0, 0.2) | 0.0 (0.0, 1.4) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.2) | 0.2 (0.0, 2.4) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.6) | 0.0 (0.0, 2.0) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.0) |
| Unknown | 102 | 24 | 29 | 8 | 81 | 12 | 6 | 32 | 52 | 18 | 21 | 56 | 67 | 0 | 60 | 5 | 18 | 12 | 758 | 0 | 2 | 155 | 9 | 133 | 84 | 9 | 75 | 45 | 6 | 247 | 75 | 0 | 0 | 13 | 58 | 9 | 46 | 7 | 4 | 7 | 41 | 56 | 33 | 187 | 10 | 456 | 57 | 58 | 18 |
| 1 Median (IQR) | |||||||||||||||||||||||||||||||||||||||||||||||||
Уберем отсутсвующие значения (NA)
DataSet2 <- DataSet2 %>% filter(!is.na(MinTemp)) # убираем NA из MinTemp
DataSet2 <- DataSet2 %>% filter(!is.na(MaxTemp)) # убираем NA из MaxTemp
DataSet2 <- DataSet2 %>% filter(!is.na(Rainfall)) # убираем NA из Rainfall
tbl2 <- DataSet2 %>% tbl_summary(by = Location) #описательная статистика
tbl2 | Characteristic | Adelaide, N = 3,0891 | Albany, N = 2,9281 | Albury, N = 3,0021 | AliceSprings, N = 3,0291 | BadgerysCreek, N = 2,9191 | Ballarat, N = 3,0271 | Bendigo, N = 3,0301 | Brisbane, N = 3,1451 | Cairns, N = 2,9871 | Canberra, N = 3,4111 | Cobar, N = 2,9841 | CoffsHarbour, N = 2,9441 | Dartmoor, N = 2,9371 | Darwin, N = 3,1911 | GoldCoast, N = 2,9751 | Hobart, N = 3,1871 | Katherine, N = 1,4951 | Launceston, N = 3,0221 | Melbourne, N = 2,4341 | MelbourneAirport, N = 3,0091 | Mildura, N = 3,0071 | Moree, N = 2,8521 | MountGambier, N = 3,0271 | MountGinini, N = 2,8571 | Newcastle, N = 2,5651 | Nhil, N = 1,5681 | NorahHead, N = 2,9171 | NorfolkIsland, N = 2,9621 | Nuriootpa, N = 2,9841 | PearceRAAF, N = 2,7311 | Penrith, N = 2,9531 | Perth, N = 3,1921 | PerthAirport, N = 3,0091 | Portland, N = 2,9861 | Richmond, N = 2,9391 | Sale, N = 2,9981 | SalmonGums, N = 2,9431 | Sydney, N = 3,3331 | SydneyAirport, N = 3,0051 | Townsville, N = 3,0311 | Tuggeranong, N = 2,9941 | Uluru, N = 1,5171 | WaggaWagga, N = 2,9761 | Walpole, N = 2,7961 | Watsonia, N = 2,9941 | Williamtown, N = 2,5481 | Witchcliffe, N = 2,9431 | Wollongong, N = 2,9741 | Woomera, N = 2,9841 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MinTemp | 12 (9, 16) | 13 (10, 16) | 9 (5, 14) | 14 (7, 20) | 11 (7, 16) | 7 (4, 10) | 8 (4, 13) | 17 (13, 20) | 22 (19, 24) | 7 (2, 12) | 13 (7, 19) | 15 (11, 18) | 9 (6, 11) | 24 (22, 25) | 18 (14, 21) | 9 (6, 12) | 22 (17, 25) | 8 (4, 12) | 12 (9, 15) | 10 (7, 13) | 10 (6, 15) | 13 (7, 19) | 9 (6, 11) | 3 (-1, 8) | 14 (10, 18) | 8 (5, 12) | 16 (12, 19) | 17 (15, 19) | 9 (6, 13) | 12 (9, 16) | 13 (8, 17) | 13 (9, 17) | 13 (9, 16) | 10 (7, 12) | 12 (7, 17) | 8 (5, 12) | 9 (5, 14) | 15 (11, 19) | 15 (11, 19) | 22 (17, 24) | 7 (2, 12) | 15 (8, 21) | 9 (4, 15) | 12 (10, 14) | 10 (7, 13) | 13 (9, 17) | 11 (8, 14) | 15 (12, 18) | 13 (8, 18) |
| MaxTemp | 22 (17, 28) | 20 (18, 22) | 22 (16, 29) | 30 (23, 36) | 23 (19, 28) | 17 (12, 23) | 21 (15, 27) | 27 (23, 29) | 30 (28, 31) | 20 (15, 26) | 26 (19, 32) | 24 (21, 27) | 18 (15, 23) | 33 (32, 34) | 26 (23, 28) | 17 (14, 21) | 35 (33, 37) | 19 (15, 23) | 20 (16, 24) | 19 (15, 25) | 24 (18, 31) | 27 (21, 33) | 19 (15, 23) | 12 (6, 17) | 24 (20, 28) | 21 (16, 28) | 23 (19, 26) | 22 (20, 24) | 21 (15, 27) | 26 (21, 32) | 24 (20, 29) | 24 (20, 29) | 25 (20, 30) | 17 (14, 20) | 24 (20, 29) | 19 (16, 24) | 24 (19, 29) | 23 (20, 26) | 23 (20, 27) | 30 (27, 31) | 20 (15, 26) | 31 (24, 37) | 22 (16, 29) | 20 (17, 23) | 20 (15, 25) | 24 (20, 28) | 21 (18, 25) | 21 (18, 24) | 26 (20, 33) |
| Rainfall | 0.0 (0.0, 0.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.4) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.6) | 0.0 (0.0, 2.6) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.0) | 0.0 (0.0, 2.0) | 0.2 (0.0, 1.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 1.2) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.8) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.8) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.2 (0.0, 1.8) | 0.0 (0.0, 1.8) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.2) | 0.0 (0.0, 1.6) | 0.2 (0.0, 1.8) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.2) | 0.2 (0.0, 2.6) | 0.0 (0.0, 0.4) | 0.0 (0.0, 0.8) | 0.0 (0.0, 0.2) | 0.0 (0.0, 1.4) | 0.0 (0.0, 1.2) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.2) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.2) | 0.2 (0.0, 2.4) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.6) | 0.0 (0.0, 2.0) | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.0) |
| 1 Median (IQR) | |||||||||||||||||||||||||||||||||||||||||||||||||
Добавим статистику вывода (предварительно оставим только 2 штата)
# выбираем два штата Adelaide и Albury
DataSet2 <- DataSet2 %>% filter(Location == "Adelaide" |
Location == "Albury")
tbl2 <- DataSet2 %>% tbl_summary(by = Location) %>% #описательная статистика
add_p() #добавляем статистику вывода
tbl2| Characteristic | Adelaide, N = 3,0891 | Albury, N = 3,0021 | p-value2 |
|---|---|---|---|
| MinTemp | 12.1 (9.1, 15.5) | 9.1 (4.7, 14.3) | <0.001 |
| MaxTemp | 22 (17, 28) | 22 (16, 29) | 0.020 |
| Rainfall | 0.0 (0.0, 0.8) | 0.0 (0.0, 0.4) | 0.4 |
| 1 Median (IQR) | |||
| 2 Wilcoxon rank sum test | |||
Проблемы
Какие могут возникнуть проблемы с данными
library(readxl)
DataSet3 <- read_excel("Data/Баёва данные.xlsx")
View(DataSet3)
DataSet3$`32 электрода` #обратите вни ание на "неудобное" название столбца## [1] 44.29 40.00 39.05 34.29 40.48 51.43ещё проблемы - двойное наименование столбцов
library(readxl)
DataSet4 <- read_excel("Data/Горбунова данные.xlsx")## New names:
## • `` -> `...3`
## • `` -> `...5`
## • `` -> `...7`
## • `` -> `...9`View(DataSet4)
summary(DataSet4)## Группа Интактные ...3 ЧМТ
## Length:8 Length:8 Length:8 Length:8
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## ...5 Sc ...7 Sc+T
## Length:8 Length:8 Length:8 Length:8
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## ...9
## Length:8
## Class :character
## Mode :characterещё проблемы - выбор части данных
library(readxl)
DataSet4 <- read_excel("Data/Горбунова данные.xlsx",
range = "A2:C8")
summary(DataSet4)## № Обучение Проверка
## Min. :1.00 Min. : 5.00 Min. :180
## 1st Qu.:2.25 1st Qu.:11.25 1st Qu.:180
## Median :3.50 Median :15.50 Median :180
## Mean :3.50 Mean :19.00 Mean :180
## 3rd Qu.:4.75 3rd Qu.:19.00 3rd Qu.:180
## Max. :6.00 Max. :48.00 Max. :180